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Cancer diagnosis with DNA molecular computation


Early and precise cancer diagnosis substantially improves patient survival. Recent work has revealed that the levels of multiple microRNAs in serum are informative as biomarkers for the diagnosis of cancers. Here, we designed a DNA molecular computation platform for the analysis of miRNA profiles in clinical serum samples. A computational classifier is first trained in silico using miRNA profiles from The Cancer Genome Atlas. This is followed by a computationally powerful but simple molecular implementation scheme using DNA, as well as an effective in situ amplification and transformation method for miRNA enrichment in serum without perturbing the original variety and quantity information. We successfully achieved rapid and accurate cancer diagnosis using clinical serum samples from 22 healthy people (8) and people with lung cancer (14) with an accuracy of 86.4%. We envision that this DNA computational platform will inspire more clinical applications towards inexpensive, non-invasive and rapid disease screening, classification and progress monitoring.

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Fig. 1: DNA computation platform for NSCLC diagnosis.
Fig. 2: Amplification and transformation of miRNAs to loop DNAs.
Fig. 3: Workflow of the DNA computation.
Fig. 4: Validation of the DNA computation-based diagnostic system with synthetic and clinical samples.

Data availability

The data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request. Furthermore, the miRNA-seq data used in this study are available on TCGA database

Code availability

The SVM training and validation code used in this study is from ref. 41 and available on


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This work was financially supported by the National Natural Science Foundation of China (grant nos. 21974087 and 31871009), Shanghai Municipal Education Commission—Gaofeng Clinical Medicine Grant Support (no. 20181709), Innovative Research Team of High-Level Local Universities in Shanghai, Faculty Start-up Funding Support from the Institute of Molecular Medicine of Shanghai Jiao Tong University and the Recruitment Programme of Global Youth Experts of China. We thank M. Zhang for the help on constructing kinetic models and data simulations. We thank J. Sun for the helpful discussion.

Author information




C.Z. and D.H. conceived and designed the experiments. C.Z. carried out the assays and analysed the results. C.Z., Y.Z., X.X., H.L., R.X., Y.M. and D.H. supported the optimization of assays and prepared the data. X.T. and Y.D. collected the specimens. D.H., H.-C.L. and C.Z. wrote the manuscript. D.H. supervised the project.

Corresponding author

Correspondence to Da Han.

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The authors declare no competing interests.

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Peer review information Nature Nanotechnology thanks Tom de Greef and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Note, Figs. 1–20, Tables 1–4 and refs. 43–47.

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Zhang, C., Zhao, Y., Xu, X. et al. Cancer diagnosis with DNA molecular computation. Nat. Nanotechnol. 15, 709–715 (2020).

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